Data Science and Computational Intelligence MSc 2021/22 entry

Your main study themes are:

• Introduction to Statistical Methods for Data Science: This module aims to provide you with knowledge of widely used statistical methods, and their application, in data science. On successful completion of this module, you should have learned the fundamental principles of probability theory and statistics, including distribution theory, required on the course. In addition, this module aims to introduce distribution theory and statistical inference, including estimation, maximum likelihood estimators, hypothesis testing, and the foundation of Bayesian inference. This module then introduces important general aspects of statistical modelling and data analysing for computer and simulation experiments. The general principle of a wide range of well-known statistical models will be explored in detail and their usage to solve some real world applications will be demonstrated using the modern statistical software.
• Intelligent Information Retrieval: Information retrieval is among the core activities driving some of the world’s successful and high-tech businesses including Google, Facebook, and Twitter. This module aims to expose you to a range of common information retrieval methods, including both theory and practice. The module mainly emphasises text retrieval, covering only a brief outline of multimedia information retrieval. In addition, it emphasises data mining methods, especially for text classification and document clustering problems. Course work requires implementation in a computer programming language.
• Data Management Systems: This module aims to provide you with a sound knowledge of the theoretical and practical underpinnings of data management systems in centralised and distributed environments. This module is motivated by the need to harness the potential of traditional systems and of modern schemes, which arose in response to the challenges posed by big data. The organisation and delivery of the module implements a two-pronged approach, through the investigation of data models and through the study and application of relational databases and NoSQL databases. The study of distributed frameworks such as Hadoop, and relevant distributed techniques such as sharding and replication, will aim to provide further enhancement to the scope of the module.
• Machine Learning and Data Mining: Machine learning is the process whereby systems learn by identifying structures and patterns within data. As such, it has proved an important tool in various applications, including data mining, games design, diagnosis and natural language processing. We cover a broad field of intelligent techniques and their associated algorithms, such as data preparation and pre-processing, reinforcement learning, supervised and unsupervised learning, classification and clustering, applications of machine learning and future developments.
• Artificial Neural Networks: This module introduces the concepts used in neural networks and their application to solving real-world problems. Neural networks, a popular machine learning approach that attempts to model how the human brain works, is used in a wide range of applications, including image processing, speech and natural language processing, medical diagnosis, bioinformatics and computational biology, emotion recognition, robotics and control. We will explore different neural network computational approaches and structure, neural network learning and teaching, convolutional neural networks, deep learning and applications of neural networks in plenty of industrial settings.
• Big Data Management and Data Visualisation: Organisations and businesses are being inundated with very large volumes of data - structured and unstructured - on a daily basis. This data is too big and complex for processing and analysing using well known traditional methods. This module aims to introduce you to the current management and visualisation methods for big data. Cutting edge techniques will be taught which will enable you to discover patterns, relationships and associations in big data sets. You will have the opportunity to engage with the emerging critical issues within the context of traditional database management systems which make them unsuitable to process big data. Thus, the nature of big data, recognised by its volume, velocity and variety, which prevents analysis in the normal setting of a traditional database, will be studied and advanced analytical techniques required to understand big data will be covered.
• Advanced Machine Learning: This module aims to provide you with an understanding of more advanced concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module is structured around recent developments on: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and topic modelling, and probabilistic graphical models as a powerful framework for representing complex domains using probability distributions. This module will also introduce evolutionary algorithms and fuzzy systems from an application oriented standpoint. We aim to study the key concepts of fuzzy sets as a methodology for handling imprecise and uncertain information. Applications of fuzzy systems and fuzzy controllers will be discussed along with coverage of their use in hybrid intelligent systems in combination with other Computational Intelligence (CI) techniques.
• Individual Research Project Preparation: This module provides the background in study skills and research methods needed to enable you to carry out an applied or enquiry-based research project.
• Master’s Project: You will undertake an overarching project under the supervision of one of our highly qualified academics. The project will cover several aspects of the studied techniques and implementation from the taught modules. Guided by an expert tutor, this project helps to develop your research and practical skills and gain experience similar to that of a data scientist professional. Previous student projects have included: analysing big data for the movie industry; oil extraction and refinement analysis; customers’ emotion recognition; speech and tone recognition; text sentiment analysis; performing automatic scene labelling and object recognition; fuzzy controllers; and designing a neural networks robotics controller for helping the elderly.

A 15-credit module will typically have around 30 hours of contact time associated with it. This will include a combination of, but may not be limited to, lectures, small-group sessions, laboratory sessions and support sessions. In addition, you will be expected to undertake significant self-directed study each week, depending on the demands of individual modules.

As an innovative and enterprising institution, the University may seek to utilise emerging technologies within the student experience. For all courses (whether on-campus, blended, or distance learning), the University may deliver certain contact hours and assessments via online technologies and methods.

If you have a desire to travel, it is possible to spend a period abroad for part of your studies, for as little as two weeks. We also offer you the chance to participate in field trips to a number of different overseas locations, which have previously included China, Poland, Spain and Finland.

Please note that we are unable to guarantee any placement or study abroad opportunities and that any such opportunities referred to on this webpage may be subject to additional costs (e.g. travel, visas and accommodation etc.), competitive application, availability and/or meeting any applicable visa requirements. To ensure that you fully understand the requirements in this regard, please contact the International Office for further details if you are an EU or International student.